Adaptive genetic algorithms applied to dynamic multiobjective problems

This paper describes an adaptive genetic algorithm (AGA) with dynamic fitness function for multiobjective problems (MOPs) in a dynamic environment. In order to see performance of the algorithm, AGA was applied to two kinds of MOPs. Firstly, the algorithm was used to find an optimal force allocation for a combat simulation. The paper discusses four objectives that need to be optimized and presents a fuzzy inference system that forms an aggregation of the four objectives. A second fuzzy inference system is used to control the crossover and mutation rates based on statistics of the aggregate fitness. In addition to dynamic force allocation optimization problem, a simple example of a dynamic multiobjective optimization problem taken from Farina et al. [M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evol. Comput. 8 (5) (2004) 425–442] is presented and solved with the proposed algorithm. The results obtained here indicate that performance of the fuzzy-augmented GA is better than a standard GA method in terms of improvement of convergence to solutions of dynamic MOPs.